Fire monitoring

ABSTRACT

Tools for fire monitoring are presented. One method includes an operation for accessing values of features for monitoring a fire in a region. The features include satellite images at a first resolution, vegetation information, and weather data. Further, each satellite image includes first cells associated with the geographical region and the first resolution defines a first size of each first cell. The method further includes generating a map of the geographical region comprising a plurality of second cells having a second size, which is smaller than the first size. Additionally, the method includes operations for estimating, using a machine-learning model, probability values for the second cells in the map based on the features, each probability value indicating if the second cell contains an active fire, and for updating the map based on the probability values for the second cells. The map is presented in a user interface.

RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 16/197,503, filed Nov. 21, 2018;U.S. patent application Ser. No. 16/197,505, filed Nov. 21, 2018; andU.S. patent application Ser. No, 16/197,547, filed Nov. 21, 2018, thecontents of each which are incorporated by reference in theirentireties.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to methods,systems, and machine-readable storage media for managing fire operationsand, more particularly, to methods, systems, and machine-readablestorage media for monitoring active fires.

BACKGROUND

When fighting large forest fires, it is important to understand thespread of the fire as well as predict the evolution of the fire based oncurrent conditions. If fire managers know where the fire is and wherethe fire is going, the fire managers may make decisions on how to fightthe fire or evacuate people from danger areas in order to avoid life andproperty losses.

Current methods for monitoring fire provide very limited information.For example, some estimators provide one update about the fire situationevery 24 hours, and then the planning is done for the next 24 hours.However, conditions may change quickly (e.g., weather changes) that mayalter the fire path, but the fire manager will not get updates for along time, which complicates the decision-making for fighting the fireand evacuating people. Additionally, most times the fire managers do nothave information about residents in the path of the fire, such as numberof people, age distribution, hospitals, schools, senior residences, etc.

Firefighting strategies usually have a tree-structure management chainwhere goals are set at the top and the implementation is decided furtherdown at the lower levels to try to meet the management goals. But if thegoals handed down from the top do not have the specifics about locationswhere to fight the fire in the timelines for fighting the fire, thedecision-making will be ineffective.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and cannot be considered aslimiting its scope.

FIG. 1 illustrates the components of a comprehensive fire managementstrategy, according to some example embodiments.

FIG. 2 illustrates example factors when planning for file monitoring,according to some example embodiments.

FIG. 3 illustrates decision-making and firefighter-safetyconsiderations, performed by a computer system, when monitoring activefires, according to some example embodiments.

FIG. 4 is a high-level hardware architecture for fire monitoring,according to some example embodiments.

FIG. 5 illustrates ember modeling, by the computer system, to determinefire spread, according to some example embodiments.

FIG. 6 is a user interface, of the computer system, for monitoringwildfires based on current conditions, according to some exampleembodiments.

FIG. 7 is a user interface, of the computer system, for viewing theevolution of wildfires, according to some example embodiments.

FIG. 8 is a user interface, of the computer system, for tracinghistorical behavior of an active fire, according to some exampleembodiments.

FIG. 9 is user interface, of the computer system, with a slidingtimeline for time-stamp predictions of the active fire path, accordingto some example embodiments.

FIG. 10 is a user interface, of the computer system, with a selectedview of wind parameters, according to some example embodiments.

FIG. 11 is a user interface, of the computer system, with a selectedview of instability parameters, according to some example embodiments.

FIG. 12 is user interface, of the computer system, for viewing fuelparameters, according to some example embodiments.

FIG. 13 is a user interface, of the computer system, showing block-levelrisk of an urban area, according to some example embodiments.

FIG. 14 illustrates hardware architecture for continuously updating thefire footprint, according to some example embodiments.

FIG. 15 illustrates satellite images of the fire obtained in differentfrequency bands, according to some example embodiments.

FIG. 16 illustrates hardware architecture for monitoring a fire,according to some example embodiments.

FIG. 17 illustrates the training and use of a machine-learning program,of the computer system, according to some example embodiments.

FIG. 18 is a diagram illustrating hardware architecture for forecastinga fire, according to some example embodiments.

FIG. 19 illustrates a workflow of an ensemble forecast, performed by thecomputer system, according to some example embodiments.

FIG. 20 illustrates the data-assimilation process, performed by thecomputer system, according to some example embodiments.

FIG. 21 illustrates a fire spread model utilizing a machine-learningapproach, performed by the computer system, according to some exampleembodiments.

FIG. 22 is hardware architecture to mitigate fire spread, according tosome example embodiments.

FIG. 23 is hardware architecture for fire-protection planning, accordingto some example embodiments.

FIG. 24 is a flowchart of a method for monitoring the spread of a fire,according to some example embodiments.

FIG. 25 is a flowchart of a method for updating the fire spread modelbased on fire-status updates, according to some example embodiments.

FIG. 26 is a flowchart of a method for providing a user interface tomonitor the spread of fire over time, according to some exampleembodiments.

FIG. 27 is a block diagram illustrating an example of a machine upon orby which one or more example process embodiments described herein may beimplemented or controlled.

DETAILED DESCRIPTION

Example methods, systems, and computer programs are directed to toolsfor fire monitoring. Examples merely typify possible variations. Unlessexplicitly stated otherwise, components and functions are optional andmay be combined or subdivided, and operations may vary in sequence or becombined or subdivided. In the following description, for purposes ofexplanation, numerous specific details are set forth to provide athorough understanding of example embodiments. It will be evident to oneskilled in the art, however, that the present subject matter may bepracticed without these specific details.

A fire monitoring tool provides information about the state of awildfire. The information may be presented in a user interface thatincludes maps with details about the fire scar, the fire perimeter,weather conditions (e.g., wind), instability regions, structures, etc.The fire monitoring tool is configured to receive updates and the firestate is updated quickly to reflect the current situation without havingto wait a day for the next fire status update.

In some embodiments, satellite images are used to monitor the fire. Thecells of the fire monitoring map have higher resolution than thesatellite images, and a machine-learning tool is used to improve theresolution provided by the satellite images by combining the imageinformation with additional fire-related data (e.g., weather data,vegetation data, etc.).

A fire forecasting tool generates forecasts for the evolution of thefire, which include the evolution of the fire perimeter and possible newignition sites. The fire forecasting information may be presented on auser interface that includes maps with the details about the fireevolution. The user interface includes a time bar where the user mayselect the time for the forecast. The fire forecasting tool utilizes theinformation from the fire monitoring tool to provide forecasts based onthe current available information. Additionally, the fire forecastingtool may receive status updates regarding the fire situation and updatethe fire forecast right away to obtain an updated fire forecast.

In one embodiment, a method includes an operation for accessing adatabase to obtain values for a plurality of features associated with afire in a geographical region. The plurality of features include one ormore satellite images at a first resolution, vegetation information forthe geographical region, and weather data for the geographical region.Each satellite image comprises a plurality of first cells associatedwith the geographical region, the first resolution defining a first sizeof each first cell. The method further includes an operation forgenerating a map of the geographical region. The map comprises aplurality of second cells having a second size, where the second size issmaller than the first size. Additionally the method includesestimating, using a machine-learning model, probability values for thesecond cells in the map based on the plurality of features. Eachprobability value indicates if the second cell contains an active fire.Further, the method includes operations for updating the map of thegeographical region based on the probability values for the secondcells, and for causing presentation of the map in a user interface.

In another embodiment, a system includes a memory comprisinginstructions and one or more computer processors. The instructions, whenexecuted by the one or more computer processors, cause the one or morecomputer processors to perform operations that include accessing adatabase to obtain values for a plurality of features associated with afire in a geographical region. The plurality of features comprise one ormore satellite images at a first resolution, vegetation information forthe geographical region, and weather data for the geographical region.Each satellite image comprises a plurality of first cells associatedwith the geographical region, where the first resolution defines a firstsize of each first cell. The operations also include generating a map ofthe geographical region, the map comprising a plurality of second cellshaving a second size, where the second size is smaller than the firstsize. The operations further include estimating, using amachine-learning model, probability values for the second cells in themap based on the plurality of features, each probability valueindicating if the second cell contains an active fire. The operationsfurther include updating the map of the geographical region based on theprobability values for the second cells, and causing presentation of themap in a user interface.

In yet another embodiment, a machine-readable storage medium (e.g., anon-transitory storage medium) includes instructions that, when executedby a machine, cause the machine to perform operations that includeaccessing a database to obtain values for a plurality of featuresassociated with a fire in a geographical region. The plurality offeatures includes one or more satellite images at a first resolution,vegetation information for the geographical region, and weather data forthe geographical region, Each satellite image comprises a plurality offirst cells associated with the geographical region, the firstresolution defining a first size of each first cell. The operationsfurther include generating a map of the geographical region. The mapcomprises a plurality of second cells having a second size, where thesecond size is smaller than the first size. Additionally, the operationsinclude estimating, using a machine-learning model, probability valuesfor the second cells in the map based on the plurality of features. Eachprobability value indicates if the second cell contains an active fire,Further, the operations include updating the map of the geographicalregion based on the probability values for the second cells, and causingpresentation of the map in a user interface.

In one example embodiment, a method is provided for receiving, via acomputer network and by a fire forecasting system, fire-related inputsincluding vegetation data, topography data, weather data, andfire-monitoring information including data identifying a physical shapeof a fire burning in a region. Further, the method generates, by thefire forecasting system, fire forecast data for the region based on thefire-related inputs. The fire forecast data describes a state of thefire in the region at multiple times in the future. The state of thefire comprises a fire perimeter, a fire line intensity, and a flameheight. Additionally, the method includes receiving, via the computernetwork, updated fire-monitoring information regarding a current stateof the fire in the region. The fire forecasting system modifies the fireforecast data based on the updated fire-monitoring information. Further,the method causes presentation, in a user interface, of a fire forecastbased on the fire forecast data.

In another example embodiment, a fire forecasting system includes amemory comprising instructions and one or more computer processors. Theinstructions, when executed by the one or more computer processors,cause the one or more computer processors to perform operations thatinclude receiving, via a computer network and by a fire forecastingsystem, fire-related inputs including vegetation data, topography data,weather data, and fire-monitoring information including data identifyinga physical shape of fire burning in a region. The operations furtherinclude generating, by the fire forecasting system, fire forecast datafor the region based on the fire-related inputs. The fire forecast datadescribes a state of the fire in the region at multiple times in thefuture, and the state of the fire comprises a fire perimeter, a fireline intensity, and a flame height. The operations further includereceiving, via the computer network, updated fire-monitoring informationregarding a current state of the fire in the region, and modifying, bythe fire forecasting system, the fire forecast data based on the updatedfire-monitoring information. Additionally, the operations includecausing presentation in a user interface of a fire forecast based on thefire forecast data.

In yet another example embodiment, a machine-readable storage medium(e.g., a non-transitory storage medium) includes instructions that, whenexecuted by a machine, cause the machine to perform operations thatinclude receiving, via a computer network and by a fire forecastingsystem, fire-related inputs including vegetation data, topography data,weather data, and fire-monitoring information including data identifyinga physical shape of fire burning in a region The operations furtherinclude generating, by the fire forecasting system, fire forecast datafor the region based on the fire-related inputs. The fire forecast datadescribes a state of the fire in the region at multiple times in thefuture, and the state of the fire comprises a fire perimeter, a fireline intensity, and a flame height. The operations further includereceiving, via the computer network, updated fire-monitoring informationregarding a current state of the fire in the region. Further, theoperations include modifying, by the fire forecasting system, the fireforecast data based on the updated fire-monitoring information, andcausing presentation in a user interface of a fire forecast based on thefire forecast data.

In an example embodiment, a method includes estimating, by a firemanagement system, a fire state in a region and a forecast of anevolution of a fire at a plurality of times. The fire management systemprovides a user interface for presenting fire information based on theestimated fire state and the forecast. The user interface includes a mapof the region, a graphical representation of the fire information, and atime bar for selecting a time of the plurality of times for the fireinformation. Further, the method includes receiving, via the userinterface, a selection of the time period for the presentation of thefire information. The selected time may be a past time, a present time,or a future time. The fire management system causes presentation in theuser interface of the fire information for the selected time.

In another embodiment, a system includes a memory comprisinginstructions and one or more computer processors. The instructions, whenexecuted by the one or more computer processors, cause the one or morecomputer processors to perform operations that include estimating, by afire management system, a fire state in a region and a forecast of anevolution of a fire at a plurality of times. The operations also includeproviding, by the fire management system, a user interface presentingfire information based on the estimated fire state and the forecast. Theuser interface includes a map of the region, a graphical representationof the fire information, and a time bar for selecting the time periodfor the presentation of the fire information. Further, the operationsinclude receiving, via the user interface, a selection of the time forthe fire information. The selected time may be a past time, a presenttime, or a future time. The operations further include causingpresentation in the user interface of the fire information for theselected time.

In yet another embodiment, a machine-readable storage medium (e.g., anon-transitory storage medium) includes instructions that, when executedby a machine, cause the machine to perform operations that includeestimating, by a fire management system, a fire state in a region and aforecast of an evolution of a fire at a plurality of times. Theoperations also include providing, by the fire management system, a userinterface presenting fire information based on the estimated fire stateand the forecast. The user interface includes a map of the region, agraphical representation of the fire information, and a time bar forselecting a time of the plurality of times fur the fire information.Additionally, the operations include receiving, via the user interface,a selection of the time period for the presentation of the fireinformation. The selected time may be a past time, a present time, or afuture time. The operations further include causing presentation in theuser interface of the fire information for the selected time.

FIG. 1 illustrates the components of a comprehensive fire managementstrategy, according to some example embodiments. Fire monitoring 102identifies the current location of the fire, and the fire location isupdated based on live updates 108 containing fire-related data. The liveupdates 108 may be of different kinds, such as satellite images, reportsfrom people on the fire front, social media information and images,emergency response lines (e.g., 911, etc.).

Fire forecasting 104 identifies the future location of the fire based onthe current fire location and additional data that provides informationon how the fire will spread. The additional data may include weatherinformation, topography of the area, types of fire fuel, etc. In someexample embodiments, a detailed map is provided with the fire-forecastinformation. The detailed map includes a time bar with a timescale thatmay be slid back and forth to select the time period, e.g., fireforecast at one hour, four hours, 12 hours, 24 hours, 48 hours, etc.

Fire forecasting 104 assists the fire manager in determining the impactof the wildfire to the communities in the fire's path in order to makewell-informed decisions on how to fight the fire and evacuate people.Further, evacuation routes may be identified for the safe transport ofpeople and fire-fighting equipment.

Fire forecasting 104 may also receive live updates 108 in order tocontinuously update the fire forecast. Fire managers do not have to wait24 hours for the next prediction based on the current available data.

Fire mitigation 106 identifies measures to reduce fire impact fromfuture fires. Fire mitigation 106 is a planning tool for communitymanagers to prepare for the impact of possible fires in theircommunities. By generating a statistical evaluation of the fire impact,community managers are able to identify measures to reduce the negativeeffects of fires. Additionally, fire mitigation 106 provides a tool formonitoring the smoke plan so controlled vegetation burns do not causeadverse health effects on the community.

Often, when firefighters arrived at a fire, they are given a map with anapproximate line of where the fire is, but the information tends to lackaccuracy; sometimes they do not even get a map, but just an approximatelocation of the fire. The tools provided herein give firefighters theability to obtain a detailed description of the location of the firewith live updates to continuously reassess the fire location (e.g., byprocessing satellite imagery) and estimates for the evolution of thefire perimeter.

FIG. 2 illustrates example factors when planning for file monitoring102, according to some example embodiments. Fire monitoring 102 tracksthe active fire line based on current conditions, such as current firelocation, weather data (e.g., wind force and direction, clouds,temperature), type of fire fuel in the path of the fire (e.g., bushes,trees, rocky terrain, urban areas), etc. Fire monitoring 102 alsomonitors the area impacted by the embers that may change the active fireline (e.g., generation of new fire spots).

Fire monitoring 102 assists firefighters to make decisions regardingfighting the fire and assisting people in evacuations. Knowing where thefire is (and where the fire is going to be) is critical information formaking these decisions.

In some example embodiments, satellite image information is used todetermine the current location of the fire. Although the satellitesprovide low-resolution images, embodiments of fire monitoring 102utilize techniques to increase the resolution on the map of the locationof the fire. This way, fire monitoring 102 is more accurate thanprevious solutions and provides faster updates based on the live data.

Fire forecasting assists firefighters in making decisions by providingthe information on where the fire is going to be in the near future. Forexample, firefighters often make decisions on evacuations based on wherethe fire is going to be in the next 12 or 24 hours. Thus, accurateinformation for fire forecasting in those time periods is critical tomake those life-saving decisions.

FIG. 3 illustrates decision-making and firefighter-safetyconsiderations, performed by a computer system, when monitoring activefires, according to some example embodiments. The live fire view, innear real-time, allows for better decision-making and improvedfirefighter safety.

Initially, the weather predictions 302 are gathered, and an ignitionforecasting method 304 predicts where fire may ignite, e.g., resultingfrom lighting or embers traveling in the air. The real-time fire map isthen calculated 306 that shows the current location of the fire. Aftercalculating 306 the real-time fire map, the behavior of the fire may bepredicted 308, as well as which population and property are vulnerable310 to the fire, and the planning of evacuating model 312 for people andanimals.

As a result of these activities, firefighter safety 314, public safety316, and decisions about evacuation or sheltering in place 318 areeasier to made because the response teams have accurate information.

For example, in case of emergency, community managers are able to comeup with a plan to prioritize rescue efforts, identifying a timeline ofevacuation for different areas. Police and other officials may lackresources to assist a large number of residents in the path of the fire.By better understanding the evolution of the fire, community resourcescan be scheduled in order to assist people with urgent needs.

FIG. 4 is a high-level hardware architecture for fire monitoring,according to some example embodiments. About 1% of fires cause most ofthe damage to people and property, and these are the fires that growlarger than a hundred hectares. The behavior of these large fires isusually difficult to predict due to changing conditions (e.g., weather)and their large size. These fires may last a month or more and requireenormous quantities of resources to fight the fire and to keep thepopulation safe.

The fire weather monitoring module 404 analyzes multiple sources offire-related information and provides information about the state of thefire. The fire weather monitoring module 404 takes multiple inputs,including the weather forecast 402, live data 406 (e.g., weather stationradar, satellite imagery, people observations, etc.), and static data407 (e.g., topography, fire fuel, buildings in the area, demographics ofthe residents, etc.).

In some example embodiments, the satellite data is combined with firemodels every 10 minutes to increase the resolution of the satelliteimages of the fire, a process that is referred to herein as satellitedownscaling. In reality, the resolution of the images is not increased,but the resolution of the output indicating if a cell (e.g.,corresponding to a pixel) is on fire is higher than the pixels in thesatellite image, although for simplicity of description it may be saidthat the resolution provided by the satellite images is increased, andsatellite downscaling refers to the decrease of the map scale comparedto the scale of the satellite images.

In some example embodiments, the fire model is a machine-learningprogram that utilizes a plurality of features for predicting the firestate. As used herein, “fire state” refers to any information about thefire. In some embodiments, the fire state includes information bygeographic cell, and the information includes whether the cell is onfire, vegetation in the cell, topography, etc. The features for themachine-learning program include, at least, weather, fuel type,topography, building structures, etc. Data from previous fires isutilized to train the machine-learning programs.

There are, at least, two types of satellites that provide imageinformation: geostationary satellites and non-stationary low-orbitsatellites. The low-orbit satellites provide better image resolution offires because these satellites are closer to the ground than thegeostationary satellites, but since the low-orbit satellites are inmotion, updates are not as frequent as with the geostationarysatellites. The National Aeronautics and Space Administration (NASA) andthe National Oceanic and Atmospheric Administration (NOAA) are twosources of satellite data.

The information from the fire weather monitoring module 404 is used bythe lightning ignition model 408 and for generating fire reports 410 forcommunity resources that fight the fire or assist people during thefire. The lightning ignition model 408 predicts where a fire may ignite,and the probability of the fire igniting.

The live monitor 414 provides user interfaces (e.g., on a display orprinted on paper) that enable the view of the fire and its evolution.Additionally, different parameters may be configured to obtain differentviews and hide or show different factors (e.g., winds, topography, firefuel).

The live monitor 414 receives inputs from the lightning ignition model408, fire reports 410, fuel updates and drought information (e.g.,drought maps) 412, and wind change and fire behavior spike detection416. Further, the live monitor 414 generates information for public andfirefighter safety decision support 418, which may be presented in areport or a user interface.

In some example embodiments, the live monitor 414 may use floodinformation to increase the accuracy of the fire prediction models. Thisway, a multi-hazard solution is provided that takes into account theeffects of flooding when fighting fire. After a flood, the terrain maychange, such as vegetation or the course of water ways. Further, after afire, the lack of vegetation may increase the risk of flooding orlandslides. By taking a multi-hazard approach, community managers areable to better decide how to plan the use of available resources forlower impact from fires and floods.

FIG. 5 illustrates ember modeling, by the computer system, to determinefire spread, according to some example embodiments. Ember includes smallpieces of burning material (e.g., bark, debris) that fly into the air,travel based on the wind direction, and then fall on the ground, causingthe possibility of starting fires at a distance from the current burningarea. In simple terms, when a structure or vegetation burns due tothermo-mechanical effects, the material loses its structural integrityand breaks off in the form of tiny particles (ember). The embers getlofted through the fire plume and are transported through the smoke toland in other places and possibly ignite other fires.

In some example embodiments, ember modeling is used to determine thespread of fire by embers, which is the primary method of fire spread inurban areas. Instead of just thinking of fire spread as an expandingborderline, ember modeling provides a more realistic approach to firespread. Some prior models treat these ember particles as compact forms(e.g., a sphere), which does not match well to reality, causing manyprediction errors. In reality, embers may take multiple forms (e.g.,cylindrical, spherical, ellipsoidal), and ember modeling takes intoconsideration multiple forms for generating the ignition forecasts.

FIG. 6 is a user interface 602, of the computer system, for monitoringwildfires based on current conditions, according to some exampleembodiments. User interface 602 shows the likelihood of wildfiresstarting based on the present situation. Diamond icons 608 show theprobability of lighting happening during the storm, which may cause newfires.

Colored areas 610-612. show where fire may ignite. Legend 604 shows thecolor legend for the risk of fire (e.g., numbered from 0 to 3, with 3being for the highest risk). Box 606 includes an option for selectingwhether to show the icons 608 representing the probability of lightning.

In another example embodiment, another box (not shown) may be providedto select whether to show fire-ignition risk due to power lines startinga fire. For example, in the presence of high winds, the power lines maycause sparks that may result in fire ignitions.

In the example illustrated in FIG. 6, the highest risk area is the areawith a higher probability of lightning strikes. Further, three differentrisk areas are identified in this region. Thus, as illustrated in FIG.6, fire monitoring is not just about monitoring a current fire, but alsoabout monitoring conditions to estimate the risk of a fire starting.

FIG. 7 is a user interface 702, of the computer system, for viewing theevolution of wildfires, according to some example embodiments. The userinterface 702 illustrates the evolution of the live fire; that is, theuser interface 702 provides a representation of the fire forecast. Themap shows the burnt scar 710 (area that has already burnt), the activefire 712 (area where the fire is live), and the ember area 708 (areawhere ember may fall and ignite new fires). Legend box 704 illustratesthe different types of fire areas.

A time bar 706 is provided to select the time for the presentation ofthe fire map, which may be a time in the past to show how the fire hasevolved, or a time in the future to show the fire forecast at differenttimes. The time scale may be configured for different time steps, suchas thirty minutes, an hour, two hours, twelve hours, etc.

By using the time bar 706, the user transitions seamlessly between thefire monitoring aspect and the fire prediction aspect, easily blendingthe past with the future of the fire.

In some previous fire prediction tools, a map is provided with a lineindicating the active fire line and a line predicting where the fire maybe later (e.g., in 24 hours). The prediction is done once a day (or inlonger periods such as a week) and there are no live updates based onadditional information. Also, these predictions fail to consider theweather and changes in the weather (such as prevailing wind direction).

The fire prediction tool is not a statics tool because it takes updateson fire-related data to continuously improve fire predictions. The fireprediction tool is continuously updating the forecast as additional databecomes available.

FIG. 8 is a user interface 802, of the computer system, for tracinghistorical behavior of an active fire, according to some exampleembodiments. In FIG. 8, the time bar 706 has been moved back to −30minutes from the current time. When comparing with the illustration inFIG. 7, it can be seen that the size of the fire is smaller and that themap now shows where the fire originated.

FIG. 9 is user interface 902, of the computer system, with a slidingtimeline for time-stamp predictions of the active fire path, accordingto some example embodiments. In FIG. 9, the time marker in the time bar706 has been moved two hours into the future from the current time. Whencomparing with the illustration in FIG. 7, it can be observed how thefire will grow and in which directions. In this example, there are nowtwo active fire fronts, one to the north and one to the east, withrespective ember lines where new fires may ignite due to ember spread.

FIG. 10 is a user interface 1002, of the computer system, with aselected view of wind parameters, according to some example embodiments.The fire monitor user interface includes options for presenting oromitting different types of information that affects fire. In someexample embodiments, the fire monitor user interface includes selectableview parameters for wind (FIG. 10), instability (FIG. 11), fuel (FIG.12), rain, temperature, fumes, etc.

The user interface 1002 shows wind information. A plurality of arrows inthe map show the direction of the wind at the different locations. Whena fire manager is trying to make decisions about the tire, winddirection is important to assessing the spread of the fire. If the winddirection is constant, then planning is easy. However, when conditionschange, such as a wind-direction change, then danger increases becauseof the unexpected evolution of the fire that may catch by surpriseresidents and firefighters in the area. This is why it is important tomonitor wind direction throughout the region.

Additionally, the fire itself may create changes in the local winds,creating instability that may change fire behavior very drastically in ashort amount of time. Firefighters may lose their lives because of thesequick changes in the fire spread.

In the example embodiment illustrated in FIG. 10, the fire monitor hasdetected a special area where wind direction from one area meets adifferent wind direction from another area. Boundary line 1004 shows aspecial boundary where the two wind directions meet: winds on an easterndirection meet winds on a northern direction. This is important for thefirefighters because if the fire reaches this boundary line 1004, thenthe fire spread will change direction and possibly create difficultiesfor firefighters and residents in the area.

It is noted that if the time is moved forward on the time bar 706, thenit is possible to see how the fire spread will change when the firemeets the boundary line 1004. The fire manager may decide not to sendfirefighters to this problematic area to avoid danger.

FIG. 11 is a user interface 1102, of the computer system, with aselected view of instability parameters, according to some exampleembodiments. As the fire interacts with the weather and the environment,areas of instability 1104 may be created where the fire may exhibitvolatile behavior. The fire forecast identifies these areas ofinstability 1104 to assist the fire manager to avoid problematic areasthat may cause injury due to a sudden change in the evolution of thefire footprint or the speed of propagation.

To determine the areas of instability 1104, the fire forecast analyzesmultiple factors, such as weather (e.g., wind), topography, fuel for thefire, etc., to determine when these conditions might signal a change inthe fire pattern or an acceleration of the fire spread.

In some example embodiments, the area of instability 1104 is determinedbased on a change in the fire spread beyond a certain threshold (e.g.,rate of spread (ROS) increases by twenty percent or more) or when thedirection of the fire spread changes (e.g., prevailing direction of thefire changes more than 30 degrees).

FIG. 12 is user interface 1202, of the computer system, for viewing fuelparameters, according to some example embodiments. The user interface1202 provides a color-coded representation of the fuel materials in theregion. Legend box 1204 describes the different types of material, whichmay include one or more of grass, ground, retem, road, rocks, shadow,short grass, shrub, thymus, trees, wet grass, etc.

By identifying the type of fire fuel in the direction of the firespread, it is easy to understand the fire evolution and to plan for thetransport of people and equipment in the area.

FIG. 13 is a user interface 1302, of the computer system, showingblock-level risk of an urban area, according to some exampleembodiments. The user interface 1302 shows a map encompassing severalcity blocks and a window 1304 with demographic information about thepeople in the selected area covered by the map.

Each of the blocks in the city are assigned a risk level from 0 to 3,with 3 being the highest risk level. The risk level is color-coded forthe block, as described in the legend box 1306.

The window 1304 with demographic information includes information aboutseniors in the area, number of families with low income, number ofpeople with visual impairment, and number of children. For the seniorgroup, the window 1304 shows the risk level, the estimated number ofseniors, and the estimated percentage of the population that areseniors. Similarly, low-income families show the risk level, the numberof persons with low income, and the percentage of the population. Thesame information is also provided for visual impairment and children.

The risk level represents the probability of loss because of fire. Arisk level of 0 represents no damage or very little damage, a risk levelof 1 represents some damage, a risk level of 2 represents substantialdamage, and a risk level of 3 represents total loss.

The window 1304 with demographic information assists the communityleaders in planning help for people at a higher risk. By knowing wherethe most sensitive population is located, the community leaders mayprioritize the often-scarce resources to assist these people first.

Also, by using the timeline bar (not shown in FIG. 13), the user maydetermine the time frames when the people in the different blocks mayneed assistance.

FIG. 14 illustrates hardware architecture 1400 for continuously updatingthe fire footprint, according to some example embodiments. Thearchitecture 1400 provides the detail for the satellite-downscalingmachine-learning model. As discussed above, there are two types ofsatellite images: low-resolution high-frequency (LRH) satellites, andhigh-resolution low-frequency (HRL) satellites.

One example of LRH is the GOES-R satellite. Some examples of the HRLsatellites include MODIS, VIIRS, LANDSAT, Sentinel 1, and Sentinel 2.The National Oceanic and Atmospheric Administration (NOAH) operates aconstellation of Geostationary Operational Environmental Satellites(GOES) to provide continuous weather imagery and monitoring ofmeteorological and space environment data for the protection of life andproperty across the United States. The GOES satellites provide criticalatmospheric, oceanic, climatic, and space weather products supportingweather forecasting and warnings, climatological analysis andprediction, ecosystems management, safe and efficient public and privatetransportation, and other national priorities.

The Advanced Baseline Imager (ABI) is the primary instrument on theGOES-R Series for imaging Earth's weather, oceans, and environment. ABIviews the Earth with 16 different spectral bands (compared to five onthe previous generation of GOES), including two visible channels, fournear-infrared channels, and ten infrared channels.

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a keyinstrument aboard the Terra (originally known as EOS AM-1) and Aqua(originally known as EOS PM-1) satellites. Terra's orbit around theEarth is timed so that it passes from north to south across the equatorin the morning, while Aqua passes south to north over the equator in theafternoon.

The MODIS instrument provides high radiometric sensitivity (12 bit) in36 spectral bands ranging in wavelength from 0.4 μm to 14.4 μm. Theresponses are custom tailored to the individual needs of the usercommunity and provide exceptionally low out-of-band response. Two bandsare imaged at a nominal resolution of 250 m at nadir, with five bands at500 m, and the remaining 29 bands at 1 km. A ±55-degree scanning patternat the EOS orbit of 705 km achieves a 2,330-km swath and provides globalcoverage every one to two days. MODIS land surface products are madeavailable via the LANCE/EODIS portal.

The Visible infrared Imaging Radiometer Suite (VIIRS) 375 m active fireproduct provides data from the VIIRS sensor aboard the joint NASA/NOAASuomi National Polar-orbiting Partnership (Suomi-NPP) satellite. The 375m data. means that each pixel in the image from the satellite covers asquare with a side of 375 m. The 375 m data complements ModerateResolution Imaging Spectroradiometer (MOMS) fire detection; they bothshow good agreement in hotspot detection but the improved spatialresolution of the 375 m data provides a greater response to fires ofrelatively small areas and provides improved mapping of large fireperimeters. The 375 m data also has improved nighttime performance.Consequently, these data are well suited for use in support of firemanagement (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.

The Landsat program is the longest-running enterprise for acquisition ofsatellite imagery of Earth. On Jul. 23, 1972, the Earth ResourcesTechnology Satellite was launched. This was eventually renamed toLandsat, and the most recent, Landsat 8, was launched on Feb. 11, 2013.The images from Landsat may be viewed through the U.S. Geological Survey(USGS) ‘EarthExplorer’ website. Landsat 7 data has eight spectral bandswith spatial resolutions ranging from 15 to 60 meters; the temporalresolution is 16 days.

Sentinel-2 is an Earth observation mission developed by ESA as part ofthe Copernicus Program to perform terrestrial observations in support ofservices such as forest monitoring, land cover changes detection, andnatural disaster management. It consists of two identical satellites.The Sentinel-2 mission provides multi-spectral data with 13 bands in thevisible, near infrared, and short wave infrared part of the spectrum,and systematic global coverage of land surfaces from 56° S to 84° N,coastal waters, and all of the Mediterranean Sea.

In some example embodiments, the images from GOES-R and VIIRS are usedfor fire monitoring, but the information from any other satellite mayalso be utilized. By using VIIRS, the satellite image resolution is 375m (e.g., each pixel coves a square 375×375) and is received every 16 to18 hours, while the GOES-R resolution is about 2 Km but is available atintervals as low as five minutes. That is, the high-resolution imagesare not received very often and provide higher resolution, and thelow-resolution images are received often but provide low resolution.

The LRH images are analyzed at operation 1406 to detect the presence offire at each pixel. The LRH images from different bands are analyzed, asdescribed in more detail below with reference to FIG. 15. Based on knownpatterns of the presence of fire, a determination is made to see if eachpixel represents fire presence (e.g., 1) or if the pixel represents theabsence of fire (e.g., 0). Therefore, this is a binary classificationproblem. In general, the presence of fire will mean that a pixel isbrighter, at least, in several light bands, and the brightness may beused to determine if the pixel represents fire.

In another example embodiment, instead of a binary classification, theinformation for each pixel includes if the pixel is for an area on fire,and the intensity of the fire if applicable.

The results 1408 of the analysis at the pixel level include thedetermination of the active fire line and spot fires, the fire scar, andthe smoke plume. The determination of the active fire line and the spotfires is made by determining the boundaries of areas with fire pixels.The smoke plume is also determined from the satellite images, based onthe known conditions that result in smoke plume. For example, previousimages with smoke plume are compared to images without smoke plume todetermine the parameters for determining the presence of smoke plume.

The results 1408 are used as input for a machine-learning program 1416that uses a plurality of features 1420 to perform the satellitedownscaling 1414 and obtain the fire map 1412. More details about themachine-learning program 1416 and the features 1420 are provided belowwith reference to FIG. 17. Some of the features 1420 include thespectral bands of satellite data, weather information—includingtemperature, wind, precipitation, and drought maps—vegetation, soilmoisture content, ash and charcoal deposit maps, topography, etc.

In some example embodiments, the satellite downscaling 1414 generatesfire data for pixels with a size of 30 m (referred to herein as hi-respixels), but other pixel sizes may be implemented, such as in the rangefrom 1m to 150m. Thus, the satellite resolution of 2 Km is downscaled to30m. This means that each image pixel covers about 4444 hi-res pixels.

In some example embodiments, for each hi-res pixel, the machine-learningprogram 1416 predicts if the corresponding area is on fire or not; thatis, it is a binary classification problem. Further, the machine-learningprogram 1416 may also generate additional information, such as areas ofinstability or ember lines. For example, for each hi-res pixel, themachine-learning program 1416 provides a probability that the pixelcontains embers that may ignite a fire.

The machine-learning program 1416 is trained with data from past fires.The feature data is collected for previous fire events (e.g., satelliteimages, weather data, vegetation, etc.) at different times and this datais used for the training. Further, in some example embodiments, Gaussiansmoothing is applied to the hi-res pixels so that transitions aresmoother to avoid or reduce random fire pixels.

Once the hi-res pixels are identified, the fire perimeter is determined,as well as other details of the map, such as the fire scar, the firerisk, and the evolution of the fire. Additionally, the machine-learningprogram 1416 may be executed for different points in time in order togenerate the fire forecasts for different time periods.

An update algorithm 1418 may obtain additional data and make correctionsto the fire map 1412. The update algorithm 1418 may utilize LRH and HRLimages, information from social media and power lines, updates fromfirefighters on the field, etc., to make map corrections. That is, theupdate algorithm 1418 provides live updates to update the fire maps asnew information is received. instead of having to wait 24 hours for anew prediction, the live updates may update the fire maps every 30minutes, although other frequencies maybe used. In addition, the fireupdates may be performed on demand from the user as new information ismade available.

In some example embodiments, the update algorithm 1418 is also amachine-learning program that calculates the updated hi-res pixels.Additionally, more direct methods may be used. For example, if aphotograph of an area with fire information is received, the hi-respixels may be updated directly based on the photograph information.

FIG. 15 illustrates satellite images of the fire obtained in differentfrequency bands, according to some example embodiments. FIG. 15 is ablack-and-white representation of the images from the different bandsfor GOES-R Satellite Channels.

The satellite has 16 channels, also referred to as bands, that coverdifferent light wavelengths, and channels 2, 5, 6, 7, 14, and 15 areknown to be sensitive to fire or smoke. In addition to being sensitiveto fire, these channels may also provide information regardingvegetation, type of terrain, the canopy (e.g., how high trees are, howdense a forest is), weather, etc.

FIG. 15 shows some samples of images captured in the different bands foran active fire: image 1502 for channel 2, image 1504 for channel 5,image 1506 for channel 6, image 1508 for channel 7, image 1510 forchannel 14, and image 1512 for channel 15.

FIG. 16 illustrates hardware architecture for monitoring a fire,according to some example embodiments. The fire forecasting 1602predicts the evolution of the fire within the region. The fireforecasting 1602 may generate 1604 lightning and ignition forecastingand wind-change modeling. The lightning and ignition forecastingprovides probabilities of lightning and new fires in an area, asillustrated before with reference to FIGS. 6 and 7. The wind-changemodeling analyzes the wind conditions within the region and identifiespossible changes based on weather forecasting and other conditions.Additionally, the wind-change modeling may identify areas where winds indifferent directions meet, which may result in changes in the fireconditions, as illustrated above with reference to FIG. 10,

The fire forecasting 1602 may also generate fire and weather maps 1626that indicate how the fire and the weather may evolve. Further, as aresult of the predictions, a wildfire alert system 1628 may generatefire alerts, which may be sent to community managers or to residents inthe affected areas.

To monitor fire locations and fire-related incidents 1622, the featuremaps 1620 and a data-driven, physics-based, fire spread model 1624 isused, such as the model described below with reference to FIG. 18. Themonitoring of fire-related incidents 1622 generates outputs 1606, whichinclude evacuation plans, immediate impact on community resources, andidentification of the burnt area. The outputs 1606 may also determinethe smoke plume 1608 on the area and the firebrand shower 1610 that maycause new fires. The burned area data may also be used to characterizethe effectiveness of fire suppression efforts 1618. Modeling may beperformed to determine the impact of fire-mitigation efforts in thespread of the fire and the damage to people and property. Thefire-mitigation efforts may include assigning firefighting crews tocertain locations, dropping water in certain locations, burning certainareas ahead of the fire, etc.

The fire spread model 1624 may be data driven (e.g., machine-learningmodel) or a physics-based spread model, or a combination thereof. Moredetails are provided below with reference to FIGS. 17-21.

Estimates of loss (e.g., damage) and the impact of the community(referred to as loss estimates 1630) are created based on the smokeplume 1608, the firebrand shower 1610, the debris distribution model1612, and a risk mapping model 1616. For example, the loss estimates1630 may include the burn scar and buildings damaged by the fire. Therisk mapping model 1616 identifies the risks associated with fire, suchas the risk of fire starting and the risk of loss. See, for example, themap of the damage by city block shown in FIG. 13.

A belief propagation model 1632 predicts the evolution of the fire inthe area, and the results may be used by the risk mapping model 1616.Additionally, the loss estimates 1630 may be utilized for a wildfirerecovery program 1614 to recover forest areas after large wildfires.

FIG. 17 illustrates the training and use of a machine-learning program,of the computer system, according to some example embodiments. In someexample embodiments, machine-learning programs (MLPs), also referred toas machine-learning algorithms or tools, are utilized to performoperations associated with fire monitoring and forecasting.

Machine learning is a field of study that gives computers the ability tolearn without being explicitly programmed. Machine learning explores thestudy and construction of algorithms, also referred to herein as tools,that may learn from existing data and make predictions about new data.Such machine-learning tools operate by building a model from exampletraining data 1712 in order to make data-driven predictions or decisionsexpressed as outputs or assessments 1720. Although example embodimentsare presented with respect to a few machine-learning tools, theprinciples presented herein may be applied to other machine-learningtools.

In some example embodiments, different machine-learning tools may beused. For example, Logistic Regression (LR), Naive-Bayes, Random Forest(RF), neural networks (NN), deep neural networks (DNN), matrixfactorization, and Support Vector Machines (SVM) tools may be used forclassification or regression.

Two common types of problems in machine learning are classificationproblems and regression problems. Classification problems, also referredto as categorization problems, aim at classifying items into one ofseveral category values (for example, is this object an apple or anorange?). Regression algorithms aim at quantifying some items (forexample, by providing a value that is a real number).

In some embodiments, example machine-learning algorithms provide aclassification of a hi-res pixel in a map, determining if the pixelcorresponds to an area on fire or not. In other example embodiments, themachine-learning program determines a probability that a hi-res pixel ison fire. If the probability is greater than or equal a predeterminedthreshold, then the pixel is considered to be on fire; otherwise, thepixel is not on fire. In some example embodiments, the threshold is aprobability of 50% of fire, but other thresholds may be utilized (e.g.,60% 75%, 90%, etc.). In other example embodiments, the machine-learningprogram determines if a high-res pixel is on fire at a certain time inthe future (e.g., the area corresponding to the pixel will be on fire intwo hours).

The machine-learning algorithms utilize the training data 1712 to findcorrelations among identified features 1702 that affect the outcome. Thetraining data 1712 includes data from previous large fires.

The machine-learning algorithms utilize features 1702 for analyzing thedata to generate assessments 1720. A feature 1702 is an individual,measurable property of a phenomenon being observed. The concept of afeature is related to that of an explanatory variable used instatistical techniques such as linear regression. Choosing informative,discriminating, and independent features is important for effectiveoperation of the MLP in pattern recognition, classification, andregression. Features may be of different types, such as numericfeatures, strings, and graphs.

The fire features 1702 may be classified as static features, which donot change (or do not change very much) over time, and dynamic featuresthat may change in a short amount of time. The static features mayinclude one or more of land use, slope, aspect, elevation, soilmoisture, canopy height, canopy cover, canopy bulk density, hour of theday, drought index, Normalized Difference Vegetation Index (NDVI), andNormalized Difference Built-up Index (NDBI).

The dynamic features include the satellite-image information andweather-related features, such as wind, precipitation, temperature,humidity, cloud coverage, etc. The dynamic features may also includeinformation obtained from satellites. Different embodiments may use allor a subset of these features or add additional fire-related features.

Land use refers to the characteristics of the land, e.g., the land isgrassland, is covered with pine trees, is an urban area, is a desert,etc. Slope defines the inclination in the terrain, which is a factor forthe spread of fire because fire moves faster when spreading uphill.

Aspect indicates the direction that the slope is facing, e.g., facingnorth. Elevation indicates the altitude of the hi-res pixel. Soilmoisture indicates the amount of water on the soil.

Canopy height indicates the height of the trees in the hi-res pixelarea, such as, for example, the average height of trees in the hi-respixel area. Canopy cover is the percentage of sunlight that does not hitthe ground because of the blocking by the canopy, :For example, an 80%canopy cover provides a large amount of cover because only 20% ofsunlight hits the ground; a 10% canopy cover means that most of thesunlight hits the ground.

Canopy bulk density indicates the density of the canopy in the area,which is similar to canopy cover, but the canopy bulk measuresvolumetrically. The time (or hour) of the day is important as the timemay affect the values of the satellite channels. Some channels appearmore distinctly than others at day or night. In order for the model tolearn this distinction, the MLP includes hour of the day as a feature.

The drought index is a measurement of dryness based on recentprecipitation and temperature. The NDVI is a simple graphical indicatorused to analyze remote sensing measurements and assess whether thetarget being observed contains live green vegetation or not. The NDBIindex highlights urban areas where there is typically a higherreflectance in the shortwave-infrared (SWIR) region, compared to thenear-infrared (NIR) region.

The weather features include wind, precipitation, temperature, humidity,cloud cover, etc. Cloud cover may be important at times because thesatellite image information may be greatly reduced in the presence ofheavy cloud cover. Therefore, considering the effect of cloud cover oninput images improves the quality of the predictions.

The machine-learning algorithms utilize the training data 1712 to findcorrelations among the identified features 1702 that affect the outcomeor assessment 1720. In some example embodiments, the training data 1712includes labeled data, which is known data for one or more identifiedfeatures 1702 and one or more outcomes.

With the training data 1712 and the identified features 1702, themachine-learning tool is trained at operation 1714. The machine-learningtool appraises the value of the features 1702 as they correlate to thetraining data 1712. The result of the training is the trainedmachine-learning program 1716.

When the machine-learning program 1716 is used to perform an assessment,new data 1718 is provided as an input to the trained machine-learningprogram 1716, and the machine-learning program 1716 generates theassessment 1720 as output. For example, the machine-learning program1716 may determine if a hi-res pixel is on fire or not, or anothermachine-learning program may determine if the high-res pixel will be onfire at a predetermined time in the future (e.g., four hours from thecurrent time).

Some testing was performed for the machine-learning program by trainingthe machine-learning program with data from several actual fires. Theperformance was then tested with data from a different known fire thatwas not included in the training data 1712. The results of themachine-learning program estimates where compared to the actual fire(e.g., fire forecasting two hours in the future, fire monitoring for thecurrent presence of fire), and the results showed good accuracy levels(e.g., using a confusion matrix and F-1 scores). Early testing resultsvaried based on the training and testing data used, showing accuracylevels between about 70% and 92%, although these results are not meantto be exclusive or limiting, but rather illustrative, and will continueto improve as additional fire data is added.

Additionally, high-resolution satellite images of the actual fire werecompared to fire monitoring maps based on the low-resolution satelliteimages, and the fire map estimates were very approximate to the actualfire maps. In other tests, the areas with fire were accurately predictedwith a slight increase in the actual size of the fire. It is expectedthat as more fire data is available and the machine-learning models arefine-tuned, their accuracy will continue improving.

It was also noted during testing that the size of fires may varyconsiderably, with some fires being small while other fires are verylarge. The behaviors of small and large fires may vary due to theirsize, so using fire data for the right fire size also improves accuracy.In some embodiments, the size of the fire may also be used as a featurefor the machine-learning programs.

Some regression models only consider each pixel separately. On the otherhand, some deep-learning models are able to correlate hi-res pixels withtheir surrounding hi-res pixels and the results tend to show smoothertransitions, but with a cost of higher complexity.

In order to update the model predictions, new satellite information isobtained, as well as values for some of the features described above.Downloading the satellite image information may be a lengthy process,but once the satellite image information is available, retraining themodel may be performed in about 30 minutes or less, although with morecomputer resources, the training time may be greatly reduced.

Therefore, updates to the fire for monitoring fire forecasting may beperformed quickly without having to wait 24 hours or more to get newforecasts based on new available information.

It is noted that the embodiments illustrated in FIG. 17 are examples anddo not describe every possible embodiment. Other embodiments may utilizedifferent features, different testing data, etc. The embodimentsillustrated in FIG. 17 should therefore not be interpreted to beexclusive or limiting, but rather illustrative.

FIG. 18 is a diagram illustrating hardware architecture for forecastinga fire, according to some example embodiments. The fire-forecastingarchitecture is multidisciplinary, combining remote-sensing, machinelearning, and physics-based modeling of the fire-spread predictiveplatform.

Fire forecasting is a high-resolution, near-live forecasting tooldesigned to equip emergency-response managers and fire departments withinformation about the estimated evolution of the fire. In some exampleembodiments, fire forecasting combines physical modeling of wildfirespread with fire machine-learning situational awareness capability toprovide reliable forecasts.

One objective of fire modeling is to model fire behavior in hopes ofproducing answers to the following questions: “How quickly and in whichdirection does the fire spread?”, “How much heat does the firegenerate?”, “How high is the flame?”, and so forth. Fire modeling alsoestimates the effects of fire, such as ecological and hydrologicalchanges, fuel consumption, tree mortality, and amount and rate ofgenerated emissions from the smoke plume.

Previous solutions for fire forecasting include physics-based models andoperational fire models. One example of the physical model is theWRF-Fire model, which aims to capture the entire known physics inaddition to the interactions between the fire and the atmosphere. Theoperational fire models, also known as rasterized fire spread models,are not designed to model the interactions between fire and atmosphere,which enables them to be much faster than full-scale models. Given thecomputational inefficiency and instability issues with full-scalemodels, the fire-spread modeling is optionally used as a component ofthe fire forecasting.

However, abilities of these models are limited for several reasons: thestatic accuracy based on semi-empirical equation of the ROS—also knownas the Rothermel ROS equation 1823, their inability to identify extremefire behavior, and being insensitive to the mitigation efforts as theoperational models work based on pre-determined fuel maps. To overcomethese limitations, present embodiments utilize a probabilisticdata-driven approach to fire forecasting.

The inputs for the fire model 1820 include the Landfire data 1810,weather data 1812, and scar-site information 1814. The Landfire data1810, provided by the wildland fire management programs of the U.S.Department of Agriculture Forest Service and U.S. Department of theInterior, provides landscape-scale geo-spatial products to supportcross-boundary planning, management, and operations. Additionally,mitigation or suppression data 1802 may be applied on the fuel models ofthe Landfire data 1810 as well as the real-time ground truth 1804.Although some embodiments are presented herein regarding the use ofLandfire data, other sources of vegetation data, topography data, andfire-fuel data may be used instead, or in combination with the Landfiredata.

The Landfire data 1810 includes information regarding vegetation andfire fuel by geography, as well as changes on the landscape caused bymanagement activities and natural disturbance. The Landfire data 1810 isa compilation of data from multiple sources, including satellite images,fire-mapping programs (e.g., burned areas), and other sources. Thevegetation information describes existing vegetation types, canopycover, and vegetation height.

The Landfire fuel data describes the composition and characteristics ofsurface and canopy fuel, such as forest canopy bulk density, forestcanopy base height, forest canopy height, forest canopy cover and firebehavior fuel models. The Landfire data 1810 also includes topographicdata regarding the geographic in the region and includes aspect (azimuthof the slope surfaces across a landscape in degrees), elevation (landheight above mean sea level), and slope (percent change of elevationover a specific area, in degrees).

In some example embodiments, the weather data 1812 is imported from theHigh-Resolution Rapid Refresh (HRRR) and MesoWest. HRRR is a NOAAreal-time 3-km resolution, hourly updated, cloud-resolving,convection-allowing atmospheric model, initialized by 3 km grids with 3km radar assimilation. MesoWest is a cooperative project betweenresearchers at the University of Utah, forecasters at the Salt Lake CityNational Weather Service Office, the NWS Western Region Headquarters,and personnel of participating agencies, universities, and commercialfirms. MesoWest provides access to the current and archived weatherobservations across the United States. MesoWest relies upon weatherobserving networks that are managed by government agencies, privatefirms, and educational institutions. The weather data 1812 may be live,streamed data as well as historical data for the region of interest.

Often, fire models accept a point of ignition as the initial conditionof the rasterized fire spread. However, due to the associateduncertainties with the early location of the fire, this point ofignition may be changed such that the current model accepts multipleignition points as well as multiple ignition sites, also known as thescar-site. By leveraging this capability, the fire forecasting benefitsfrom the most accurate early estimates of the fire location as itsinitial conditions. The outcomes of the fire monitoring products areevaluated based on the metrics and previous forecast simulations. Afterpre-processing the fire perimeters through both shape and time-dependentmetrics, the results are imported as scar-site information 1814 to thefire model 1820.

Furthermore, any other sources of perimeter data, user inputs, socialmedia analysis outputs 1806, etc., can be imported to estimate theinitial ignition site, and used for the determination of the scar-siteinformation 1814.

Information from the fire monitoring 1808 is used as inputs for the firemodel 1820 of the fire forecasting. Fire monitoring 1808 calculates,among other things, the fire perimeter 1816 and the Fire Radiative Power(FRP) 1818, which is a measure of the rate of radiant heat output from afire. The fire perimeter 1816 determines the boundaries of the area orareas that are burning or have burnt. From the fire perimeter 1816 andthe FRP 1818, two metrics 1824 are calculated: the shape of the fireregion, and a time metric that includes a time series of the evolutionof the fire. The metrics 1824 may be input directly to the fire model1820 or incorporated through the scar-site information 1814.Additionally, the outputs 1826 of the fire model 1820 may also be usedto recalculate the metrics 1824.

Other existing solutions do not take into consideration the informationfrom the fire monitoring 1808, and just develop fire-spread models thatdo not have any type of validation data points that may be checkedagainst the actual position of the fire. In some example embodiments, byutilizing the fire monitoring 1808 inputs, it is possible to refine fireforecasting with new data about the fire, as it becomes available,thereby continuously improving the results of the fire forecasting.

The fire model 1820 includes one or more fire formulas 1821, database1822, and the calculation of the ROS equation 1823. The database 1822 isfor storing fire data, such as the Landfire data, weather data, firemaps, etc. In some example embodiments, the fire model 1820 includesconcurrent programming for generating the outputs 1826. The fire model1820 is able to incorporate live dynamic features to generate quickupdates to the outputs 1826.

The ROS is the horizontal distance that the flame zone moves per unit oftime (e.g., feet per minute, meters per minute) and usually refers tothe head fire segment of the fire perimeter. However, ROS can bemeasured from any point on the fire perimeter in a direction that isperpendicular to the perimeter. Because the ROS can vary significantlyover the area of the fire, it is generally taken to be an average valueover some a predetermined period of time. The fastest ROS is along theforward moving perimeter located at the head of the fire. The slowestROS will be found on the windward (hack) side of the perimeter. The ROSalong the flanks will be intermediate between the head and backing ratesof spread. The rates of spread may be estimated by timing the passage ofthe flaming front between two landmarks of known distance apart. Todetermine the ROS within the interior of a fire, firecrackers placed atknown intervals along a transect perpendicular to the flame front may beused. Other traditional methods for measuring rates of spread involvevideography or the use of thermocouples to record the passage of theflaming front.

The ROS is directly related to the amount of heat received by the fuelsahead of the flaming zone, and the heat is a function of the energyrelease rate per unit area of fire front, where the reaction intensityIR is measured as BTU/ft²/minute.

The ROS as a function of energy release was mathematically expressed byRothermel's equation. In simple terms, the ROS equation is the heatreceived by fuels ahead of the fire (numerator) divided by the heatrequired to ignite the fuels (denominator). Rothermel's equation may beused to calculate the ROS, as follows:

ROS=I _(R) ζ(1+Φ_(W)+Φ_(S))/ρ_(η) εQ _(ig)

In this equation, ROS is the rate of spread of the flaming front, I_(R)is the reaction intensity, ζ is the proportion of the reaction intensitythat heats adjacent fuel particles to ignition, Φ_(W) is thedimensionless multiplier accounting for the effect of wind in increasingthe proportion of heat that reaches adjacent fuels, Φ_(S) is thedimensionless multiplier accounting for the effect of slope inincreasing the proportion of heat that reaches adjacent fuels, ρ_(η) isthe oven dry fuel per cubic foot of fuel bed (lb/ft), ε is thedimensionless number accounting for the proportion of a fuel particlethat is heated to ignition temperature at the time flaming combustionstarts (near unity for fine fuels and decreases toward zero as fuel sizeincreases), Q_(ig) is the heat of pre-ignition or the amount of heatrequired to ignite one pound of fuel (BTU/lb).

As Rothermel's equation shows, the ROS is strongly influenced by fuels,winds, and topography, where the ROS generally increases with increasingwind speed, slope, and amount of fine fuels.

When fire growth leads to extreme fire behavior, the fire itself becomesa fourth factor that influences the ROS. Fire can produce enough heat tomodify local winds, contribute to atmospheric instability, and causecloud development.

In some example embodiments, the simulations of the fire model beginwith the information from the fire monitoring 1808, that is, the exactcurrent situation of the fire, and then the fire forecast is calculated.

In some example embodiments, Monte Carlo simulations and time-seriesanalysis are utilized by the fire model 1820. The results are combinedto resolve the spectrum of uncertainty of the fire forecast within thewhole region.

Monte Carlo simulations, also referred to as Monte Carlo experiments,are a broad class of computational algorithms that rely on repeatedrandom sampling to obtain numerical results. Their essential idea isusing randomness to solve problems that might be deterministic inprinciple. They are sometimes used in physical and mathematical problemsand are most useful when it is difficult or impossible to use otherapproaches.

In physics-related problems, Monte Carlo simulations are useful forsimulating systems with many coupled degrees of freedom, such as fluids,disordered materials, strongly coupled solids, and cellular structures.In principle, Monte Carlo methods can be used to solve problems having aprobabilistic interpretation. By the law of large numbers, integralsdescribed by the expected value of some random variable can beapproximated by taking the empirical mean (e.g., the sample mean) ofindependent samples of the variable.

In some example embodiments, the fire model 1820 uses what is referredto as an ensemble mode that utilizes Monte Carlo simulations to capturethe combinations of weather data, fuel data, etc., to determine a timeseries describing how the fire will evolve over time, such as, forexample, within 30 minutes, one hour, six hours, 12 hours, 24 hours, 48hours, etc. If the weather is changing over time, the simulations forthe different periods of time will reflect the change in the weather.

In some example embodiments, the fire formulas 1821 may include aSurface Fire Spread with FiremodsS adjustments (Albini 1976); Crown FireInitiation Passive/Active (Van Wagner 1977); Crown Fire Spread (Cruz2005); Flame Length and Fire Line Intensity (Byram 1959); Midflame WindAdjustment

Factor (Albini & Baughman 1979) parameterized as in BehavePlus; FlamMap,FSPro, and FPA according to Andrews 2012; Fire Spread on a Raster Grid(Morais 2001), or any combination thereof. In another exampleembodiments, other fire formulas 1821 may also be utilized.

Further, different fuel models may he utilized in different embodiments,such as no dynamic loading from Anderson, and dynamic loading based onScott & Burgan.

In fire forecasting, it is useful to separate the ROS model (outputs indistance per unit time) and a fire solver which solves the spatialspread of the fire for a given time to estimate the Fire Footprint. insome example embodiments, the fire solver is GridFire, which is araster-based fire spread and severity model that may be used for bothstatic and dynamic calculations of various standard fire behaviormetrics across a landscape. For inputs, GridFire requires a stack ofco-registered raster maps for the region of interest as well as a set ofscalar values which may be input via a configuration file. In otherexample embodiments, other fire solvers may also be used.

In some example embodiments, the ROS model takes the input parameters asseveral grids (e.g., weather, fuel topography) and outputs a grid of ROSused by the Fire Solver, At a high level, the fire forecastingdevelopment process can be described by offline training, prognosticsimulations, and online training.

The offline training estimates the ROS for the fire solver given a setof training events (e.g., known previous fires). This provides amachine-learning ROS. The prognostic simulations use stochasticsimulations of fire spread by using one or more of the machine-learningROS from the offline training, the machine-learning ROS with onlinetraining updates, and the original semi-empirical ROS where machinelearning performs inadequately.

The online training updates the ROS based on the observed fire spreadestimated by Fire Monitoring and the performance of the machine-learningROS up to that point.

Since Rothermerl's original equations assume that the wind direction andslope are aligned, the effects of cross-slope winds are taken intoaccount in some embodiments.

Rothermel's formula provides no specific mechanism on how fire spreadsthrough two or higher dimensional spaces. Therefore, extrapolationrequires a trade-off between the number of dimensions and exponentiallyincreasing computational complexity of fractal searches for fire front.In some example embodiments, a raster grid view is used to be able toreduce the computational complexity while being able to work directlywith a variety of the datasets such as Landfire. This also reduces thegeometric lookup step or a priori translation to vector spaces.

The original GridFire setup provides the capability to do static firesimulations, e.g., given input parameters, GridFire generates theoutputs when the maximum runtime is reached. While this is useful forcreating risk maps using Monte-Carlo type simulations, it is notsuitable for accurate forecasting. In order to obtain reliableforecasts, the initial conditions are made as close as possible to theobservation.

In order to resolve the initial-condition problem and provide reliableforecasts, the fire model inputs archived, current, and forecast weatherdata and incorporates them into the simulations. Furthermore, theinitial conditions including the initial ignition site are set by thefire monitoring 1808 module. This removes the map search for ignitionfrom the simulation procedures and also improves forecast accuracy.Moreover, since fuel models are encoded in the model, flexibleinitialization of the ignition site enables forecasting to take intoaccount the suppression effects as well as the mitigation efforts intothe forecasting outcomes.

Using the data sets of the known fires, distributions of the variablescan be constructed, and Metropolis Monte Carlo simulations can becarried out for the given ROE, using sampling from the distributions.This leads to better cover of the uncertainty spectrum in the controlparameter matrix, which leads to improved risk maps. Given the forecastsfor the control parameters or time-history of variations, time-seriesanalysis can be carried out for the ROE.

In some example embodiments, the outputs 1826 of the fire model 1820include, at least, the termination of the fire perimeter, fire-lineintensity, and flame height.

In some example embodiments, the inputs of the fire model 1820 include aplurality of values, as described below by way of example in Table 1.

TABLE 1 Name Units Description max-runtime minutes Maximum amount oftime for generating output cell-size meters (or feet) Size of the sidesof the square cell elevation- meters (or feet) Defines terrain elevationon the matrix cell slope-matrix — Defines slope on the cell fuel-model-fuel model numbers Matrix of categorical fuel maps matrix 1-256 providedby Landfire data set canopy-height- meters (or feet) Average height ofthe canopy in matrix the cell canopy-based- meters (or feet) Datashowing how tall is the height-matrix base of the canopy, e.g., averageheight from the ground to a forest stand's canopy bottom. Specifically,the lowest height in a stand at which there is a sufficient amount offorest canopy fuel to propagate fire vertically into the canopy.crown-bulk- Kg/m³ (or lb/ft³) Density of vegetation in the celldensity-matrix canopy-cover 0-100 Percentage of ground in cell matrixcovered by vegetation wind-speed Km/hr (or mi/hr) Speed of wind in thecell wind-direction degrees from North Direction of wind in the cellfuel moisture percentage Percentage of moisture of the vegetation in thecell foliar moisture percentage Moisture content of the fuel load ateach location ellipse- Scalar Scaling factor to calculate windadjustment- (dimensionless) velocity at mid-flame height at factor thecanopy height initial-ignition- [0, 1] 1 if the cell is the ignitionsite; 0 site otherwise initial-fire-line- BTU/mt/s Intensity of the fireon the fire intensity (or BTU/ft/s) perimeter

In some example embodiments, the region being analyzed is represented byone or more matrices and a plurality of cells, where each cellrepresents a pixel. In some example embodiments, the fire model 1820utilizes concurrent programming to be able to analyze each cell inparallel. Once the data for each of the cells is calculated, the datafor all the cells is combined to obtain the information for the wholeregion.

The fire forecasting includes determining if the fire is spreading fastenough to ignite the next pixel (or hi-res pixel). This determinationdepends on several factors, such as if the pixel contains flammablematerial, the power output of the fire, wind, etc. By analyzing allpixels, it is possible to create a high-resolution map of the fireforecast.

In some example embodiments, the outputs 1826 of the fire model 1820include a plurality of values, as described below in Table 2.

TABLE 2 Name Units Description global-clock minutes Minutes elapsed froma time reference. initial-ignition- [0, 1] 1 if the cell is the ignitionsite site; 0 otherwise ignited-cells [0, 1] 0 if the cell is not onfire; 1 if the cell is on fire fire-spread- [0, 1] A matrix (map) of thematrix impacted areas by fire flame-length- meters (or feet) Averageheight of flame matrix in cell fire-line- BTU/mt/s Intensity of the fireon the intensity (or BTU/ft/s) fire perimeter

These values are calculated for each cell. In some example embodiments,the size of the cell is 30 m×30 m, but other cell sizes may be utilized.

FIG. 19 illustrates a workflow of an ensemble forecast, performed by thecomputer system, according to some example embodiments. FIG. 19illustrates the high-level workflow for the Monte Carlo simulations 1906of the fire forecasting model.

The inputs include, in some example embodiments, the weather information1902 over the region being analyzed, which includes a temporal componentof how the weather is predicted to change over the period under study inthe simulation. Additionally, the inputs include information regardingthe spatial and temporal distributions of ignition patterns 1904.

In some example embodiments, the results include the output statistics1908 and risk maps 1910. The output statistics 1908 include data for thefire forecasting, including the time series regarding the evolution ofthe fire parameters, such as location, intensity, dangerous conditions,etc. The risk maps 1910 provide details on the evolution of the fire andthe risks associated with the fire, such as danger to people andproperties, as illustrated in FIGS. 6-13.

FIG. 20 illustrates the data-assimilation process, performed by thecomputer system, according to some example embodiments. As discussedabove, the fire forecast 2004 utilizes inputs 2002 (e.g., weather data,fire data) and data regarding fire observations and suppression efforts2006 to predict the evolution of the fire.

The observations may come from firefighters on the ground, fromsatellite images, or from other aerial images. The suppression efforts,also referred to as mitigation efforts, indicate the actions taken, orthat will be implemented, to fight the fire, such as burning areas aheadof the fire, cutting trees, dumping water, etc. The impact of thesuppression efforts is also taken into account to modify the predictionof how the fire would spread were those efforts not implemented.

The data assimilation module 2008 combines the results from the fireforecast 2004 with the field observations and suppression efforts 2006in order to modify the fire predictions. The data assimilation module2008 may include modifying the shape 2010 of the fire as well as the ROSparameters 2014. At operation 2012, the fire scar is morphed (e.g.,transformed) to match the observations and update inputs 2002 to adjustthe fire forecast 2004. The results are then used as inputs 2002 againto formulate a new fire forecast 2004. This way, as new observations areavailable, the data assimilation module 2008 allows for the modificationof the fire forecast 2004 to increase in accuracy. This cycle may berepeated to continue improving the accuracy of the fire forecast 2004and present it on a map.

In some example embodiments, the fire forecast 2004 is based on a modelof GridFire, which is then modified to be able to adjust to the fireupdates. GridFire is an open source fire behavior model. As inputs,GridFire takes the Landfire topography and fuel data (or equivalentraster layers provided by the user) and weather conditions that may beinput directly, sampled from provided ranges, or looked up automaticallyfrom available online weather records. GridFire simulates fire ignitionand spread over landscapes. Individual fires may be simulated as burningfor a fixed number of hours and the resulting fire perimeter, burn type,flame lengths, fire line intensity, and spread rates examined underdifferent weather conditions. GridFire relies on large scale Monte Carlosimulation, in which hundreds, thousands, or even millions of fires aresimulated and analyzed in the aggregate. As discussed above, GridFire'soutputs include the fire perimeter, the flame length, and the fireintensity.

In some example embodiments, the GridFire model is modified to take intoconsideration the updated fire monitoring information. GridFire utilizesRasterized Fire Spread models, which are modified based on the updatedfire monitoring data. A second way in which GridFire is modified is toincorporate the dynamic weather data as input parameters.

Some fire models, including GridFire, rely on an initial ignition pointor starting point where the fire originated, and then model how the firespreads from that initial ignition point. The problem is that mosttimes, that initial ignition point is unknown and the fire model has togenerate an estimation for the ignition point before generating fireforecasting results. Depending on where the initial ignition point isselected, the results will vary substantially because the models arehighly known-linear.

By performing data assimilation, the fire forecast 2004 adjusts theGridFire equation parameters so that the current fire monitoring datamatches the estimated ignition point. That is, the ignition point maychange based on the observation data, which results in more accuratefire forecasting. Additionally, as the fire spreads, the ignition pointeffect is lowered as the fire perimeter is better known because of thefire-monitoring data.

The updating of the model may be referred to as “belief propagation,” asnew data (e.g., “beliefs” of the current situation) is available, thebelief is propagated to improve the forecasting results so thefire-monitoring in the fire-forecasting components are aligned andconsistent with each other.

Previous fire-forecasting methods typically make a forecast every 24hours or more based on the information available at the time. However,fire spread is a non-linear process, so sudden changes may occur often,such as within an hour or two of the time when the fire forecast 2004was made. The fire forecast 2004 is able to generate new forecasts often(e.g., every 30 minutes or less) as new data is available, whichimproves the ability of the firefighters to plan how to fight the fireand how to protect people and property because they have a betterunderstanding of the fire perimeter and how the fire perimeter willevolve over time.

FIG. 21 illustrates a fire spread model utilizing a machine-learningapproach, performed by the computer system, according to some exampleembodiments. The machine-learning model for fire forecasting utilizesthe fire-related features (e.g., features 1702 described above withreference to FIG. 17), including the current state of the fire, todevelop a fire spread model 2114 that produces an estimation of the fireevolution over time. Therefore, the machine-learning model does not relyon the physics-based model to determine the fire spread, although, insome example embodiments, the information from both the machine-learningmodel and the data-assimilation model may he combined to forecast thefire evolution.

The ensemble forecast model 2104 utilizes information regarding the fireboundary conditions 2108 and information from observations and theimplemented suppression efforts to generate the fire forecast.

In the architecture illustrated in FIG. 21, the ensemble forecast 2104is used as an initial forecast and the output is then used to train themachine-learning (ML) model 2110 to generate fire forecasts, e.g., thefire spread model 2114. Once the ML model 2110 is trained, it ispossible to produce fire estimates without using the ROS equations.Further, information from past fires 2116 may also be used to train theMt model 2110. That is, the ML model 2110 is initially based on the ROSequations to fine tune the prediction capabilities and the selection ofthe features and how each feature affects the ML model 2110.

Eventually, as the ML model 2110 increases accuracy, the ROS equationsare not needed, and the fire forecasting may be performed using the MLmodel 2110.

The ML model 2110 may use any of the models described above, such asLogistic Regression (LR), Naive-Bayes, Random Forest (RF), neuralnetworks (NN), deep neural networks (DNN), matrix factorization, andSupport Vector Machines (SVM). The features used for fire monitoring andfire forecasting may vary and any subset of the features described abovemay be utilized for each method. Additionally, remote sensinginformation 2112 may be used as inputs to the ML model 2110, whichincludes additional information regarding the state of the fire orconditions that may affect the fire spread.

By combining the predicting strengths of the physics-based model and theML model, the accuracy of the fire forecasting state over time isimproved.

FIG. 22 is hardware architecture to mitigate fire spread, according tosome example embodiments. “Mitigation” refers to the actions that may betaken in the community to lower the negative effects of fire in a regionor a community. One goal of implementing mitigation activities is toreduce the risk to people, property, and land.

Mitigation 2202 includes three areas: fuel management 2204, propertyrisk 2206, and fuel and land disturbance 2208. Fuel management 2204analyzes the fire fuel (e.g., combustible materials) in the region anddesigns measures to reduce the amount of fuel for the fire in order todecrease the possible destruction by fire and the spread of the fire.

One component of fuel management 2204 is forest mastication planning2212, which involves chopping and crushing vegetation to reduce theamount of fuel in the region. Another component of fuel management 2204is the addition of fuel fire breaks 2214. A typical fire break is anarea where all vegetation is eliminated to stop the spread in case offire, although other fire breaks may also be used, such as floodingareas to stop the fire spread with water barriers. Another type of fuelfire break involves managing the perimeters around homes to eliminatevegetation in the perimeter in order to avoid or delay the spread of thefire to the homes. The defense of homes may also suggest the changing ofmaterials in the home to defend against fire, such as changing rooftiles to noncombustible materials.

Risk mapping 2210 determines the probabilities that fire events may takeplace throughout the region, and the risks may then be used toprioritize the forest mastication planning 2212 and the fuel fire breaks2214.

Property risk 2206 analysis may be used to determine the properties atrisk in the area. The fuel and land disturbance 2208 is used todetermine the effects of changes in the fuel and the topology withrespect to fire effects. In some embodiments, the Normalized DifferenceVegetation Index (NDVI) is used for the monitoring of vegetationdynamics from regional to global scales. The NDVI has been usuallyconsidered a reliable indicator of plant biomass and vegetation primaryproductivity. Therefore, NDVI time series have been routinely used tomeasure vegetation dynamics and ecosystem phenology over largegeographic areas. The results of the fuel and land disturbance 2208analysis is used as another input for selecting possible fuel firebreaks 2214.

Mitigation planning 2216 includes the analysis of possible measures toreduce the impact of tires, their cost and their rewards. This way, acommunity manager may put a price on a given measure and be able tojustify the potential return (e.g., a dollar spent in mitigation mayresult in a one hundred-dollars reduction in damages in case of fire).

Mitigation planning 2216 utilizes the outputs from the forestmastication planning 2212, the fuel tire breaks 2214, and the propertyrisk 2206 to identify possible mitigation measures.

The mitigation measures may be presented in a user interface indicatingthe estimated cost and the possible rewards. For example, possible firebreaks may be shown on a map indicating the ability of each fire breakto stop fire or reduce the fire spread speed. Additionally, themitigation measures may also indicate the people at risk in thecommunity and how each of the measures will contribute to keep thepeople safe. For example, demographic information may be provided toindicate areas where elderly people may be at a high risk.

FIG. 23 is hardware architecture for fire-protection planning, accordingto some example embodiments. Preparedness 2302 relates to planning forthe future on how to build a community in order to be resilient to theeffects of fire. Preparedness 2302 includes calculating the wildfireresilience index 2304 and fire planning 2306, which includes communityplanning 2308 and institutional planning 2310.

The wildfire resiliency index 2304 is an index that indicates howresilient to fire a community is. The wildfire resiliency index 2304 isbased on a plurality of factors, such as fire fuel in the area,buildings in the area and the materials used in those buildings, weatherpatterns in the area, availability of response plans to fire events,etc.

The wildfire resiliency index 2304 includes assessments of risk 2314 atthe community, block, and house level, and may use inputs from the riskmaps 2316 as well as the standards and guidelines 2318 from the NationalFire Protection Association (NFPA) and the Insurance Institute forBusiness and Home Safety (IBHS). The wildfire resiliency index 2304 mayalso consider fire and smoke detection systems 2320 and theiravailability throughout the region in commercial buildings, governmentbuildings, and private residences.

Community planning 2308 is related to planning activities by officialsin charge of making plans for the community, such as standards for newbuildings and guidelines for improving fire resiliency in existingbuildings. Similarly, institutional planning 2310 relates to fireplanning by private institutions, such as businesses and nonprofitorganizations.

The result of the planning 2306 may be integrated into a wildfirerecovery plan 2312 that describes how to recover from the effects offire in the community.

Further, planning 2306 may take into consideration a plurality offactors, such as community level leadership mechanism and roles 2322,informed decision-making at the community and block level 2324,predetermination of evacuation routes 2326, an integrated responsesystem 2328 to manage how to respond to emergencies due to fire, and theassessment 2332 of community and block wildfire resiliency index basedon the exposure data.

The integrated response system 2328 will determine the allocation ofresources 2330 for building fire resiliency, fighting fires, andassisting the community during fires, which includes resourceprepositioning 2334 and a logistics distribution system 2336.

FIG. 24 is a flowchart of a method 2400 for monitoring the spread of afire, according to some example embodiments. While the variousoperations in this flowchart are presented and described sequentially,one of ordinary skill will appreciate that some or all of the operationsmay be executed in a different order, be combined or omitted, or beexecuted in parallel.

At operation 2402, a computer system accesses a database to obtainvalues for a plurality of features associated with a fire in ageographical region. The plurality of features comprise one or moresatellite images at a first resolution, vegetation information for thegeographical region, and weather data for the geographical region. Eachsatellite image includes a plurality of first cells associated with thegeographical region, and the first resolution defines a first size ofeach first cell.

From operation 2402, the method 2400 flows to operation 2404 forgenerating a map of the geographical region that comprises a pluralityof second cells having a second size, where the second size is smallerthan the first size.

From operation 2404, the method 2400 flows to operation 2406 where themachine-learning model estimates probability values for the second cellsin the map based on the plurality of features, each probability valueindicating if the second cell contains an active fire.

At operation 2408, the map of the geographical region is updated basedon the probability values for the second cells.

From operation 2408, the method 2400 flows to operation 2410 for causingpresentation of the map in a user interface.

In one example, the method 2400 further comprises before estimating thefire values, training the machine-learning model with values of theplurality of features from previous fires.

In one example, the plurality of features further includes land-relatedfeatures that include land use, slope, aspect, elevation, and soilmoisture.

In one example, the plurality of features further includes hour of theday, Normalized Difference Vegetation Index (NDVI), and NormalizedDifference Built-up Index (NDBI).

In one example, the vegetation information includes canopy height,canopy cover, canopy bulk density, and drought index; wherein theweather data includes wind, precipitation, temperature, humidity, andcloud cover.

In one example, the method 2400 further comprises estimating, by themachine-learning model, second cells with embers that may ignite fires;and presenting, in the map, an ember area with second cells thatcomprise embers that may ignite fires.

In one example, the method 2400 further comprises estimating a fire scararea with second cells that have already burnt; and causingpresentation, in the user interface of the map, the fire scar area.

In one example, the one or more satellite images include images in aplurality of frequency bands.

In one example, the first side size is a square with 375 meter sides andthe second size is 30 meter sides.

In one example, the method 2400 further comprises receiving observationsabout a state of the fire in the geographical region, and updating theprobability values for the second cells based on the receivedobservations.

FIG. 25 is a flowchart of a method 2500 for updating the fire spreadmodel based on fire-status updates, according to some exampleembodiments. While the various operations in the method 2500 arepresented and described sequentially, one of ordinary skill willappreciate that some or all of the operations may be executed in adifferent order, be combined or omitted, or be executed in parallel.

At operation 2502, fire-related inputs are received, via a computernetwork and by a fire forecasting system, that include vegetation data,topography data, weather data, and fire-monitoring information includingdata identifying a physical shape of fire burning in a region.

From operation 2502, the method 2500 flows to operation 2504 where thefire forecasting system generates fire forecast data for the regionbased on the fire-related inputs. The fire forecast data describes astate of the fire in the region at multiple times in the future, and thestate of the fire comprises a fire perimeter, a fire line intensity, anda flame height.

From operation 2504, the method 2500 flows to operation 2506 forreceiving, via the computer network, updated fire-monitoring informationregarding a current state of the fire in the region. At operation 2508,the fire forecasting system modifies the fire forecast data based on theupdated fire-monitoring information.

From operation 2508, the method 2500 flows to operation 2510 forpresenting in a user interface a fire forecast based on the fireforecast data.

In one example, generating the fire forecast data includes utilizing aphysical model to determine fire spread data based on the fire-relatedinputs, and combining the fire spread data of the physical model withfire spread data estimated by a machine-learning program to determine acombined fire spread data. The fire spread data is included in the fireforecast data and describes the physical shape of the fire burning inthe region for the multiple times in the future.

In one example, the region is divided into cells, wherein the fireforecast data includes an indication of whether each cell is on fire.

In one example, generating the fire forecast data includes determiningan estimated point of ignition of the fire, and modifying the fireforecast comprises adjusting the estimated point of ignition of the firebased on the updated fire-monitoring information.

Further, in one example, generating the fire forecast comprisesdetermining a rate of spread of the fire from the fire perimeter.

In one example, generating the fire forecast includes determining a scararea in the region for the multiple times in the future. The scar areaidentifies which parts of the region have already burnt.

In one example, generating the fire forecast includes identifyinggeographical blocks within the region that include residential dwellingsand determining a risk level from fire for these geographical blocks.

In one example, the region is divided into a plurality of cells and thefire forecast includes, for each cell of the plurality of cells, one ormore of slope of the terrain, fuel-model, average canopy-height in thecell, vegetation density in the cell, percentage of canopy cover in thecell, wind speed, wind direction, percentage of fuel moisture ofvegetation in the cell, foliar moisture, and a flag indicating if thecell is an initial ignition site.

In one example, generating the fire forecast includes accessinginformation from physical models for the fire spread and combining theinformation from physical models for the fire spread with estimates froma machine-learning program.

In one example, the machine-learning program utilizes values from aplurality of features selected from a group consisting of satelliteimages, vegetation information for the region, weather data, land use,slope, aspect, elevation, and soil moisture.

In one example, the vegetation data includes information fire fuel inthe region, and the topography data includes information regardingchanges on the landscape caused by management activities and naturaldisturbance.

FIG. 26 is a flowchart of a method 2600 for providing a user interfaceto monitor the spread of fire over time, according to some exampleembodiments. While the various operations in this flowchart arepresented and described sequentially, one of ordinary skill willappreciate that some or all of the operations may be executed in adifferent order, be combined or omitted, or be executed in parallel.

Operation 2602 is for estimating, by a fire management system, a firestate in a region and a forecast of an evolution of a fire at aplurality of times. From operation 2602, the method flows to operation2604 where the fire management system provides a user interface forpresenting fire information based on the estimated fire state and theforecast. The user interface includes a map of the region, a graphicalrepresentation of the fire information, and a time bar for selecting atime of the plurality of times for the fire information.

From operation 2604, the method flows to operation 2606 for receiving,via the user interface, a selection of the time for the fireinformation. The selected time is selected from a group consisting of apast time, a present time, or a future time. At operation 2608, the firemanagement program causes presentation in the user interface of the fireinformation for the selected time.

In one example, when the selected time is the past time, the userinterface shows fire-monitoring information for the past time. When theselected time is the present time, the user interface shows the firestate at the present time. When the selected time is the future time,the user interface shows fire forecast information for the selectedtime.

In one example, the map includes a burnt scar where fire has burnt, andactive-fire area, and a fire perimeter. Further, the map may includeregions with a likelihood of fire based on ignition probability, and themap may include an ember area where ember may travel from the fireburning in the region.

In one example, the time bar includes a time scale and a selector foridentifying the selection of the time for the fire information. Further,in one example, the map includes an option for showing prevailing windsin the region, wherein the map includes a boundary line showing wherewind direction changes in the region.

In one example, the map may include fire-fuel type throughout theregion. Further, the map may include an estimated fire-damage risk bycity block, wherein the user interface includes a window withdemographic information of population at risk in the region. Furtheryet, the map may include instability areas where fire exhibits volatilebehavior.

FIG. 27 is a block diagram illustrating an example of a machine 2700upon or by which one or more example process embodiments describedherein may be implemented or controlled. In alternative embodiments, themachine 2700 may operate as a standalone device or may be connected(e.g., networked) to other machines. In a networked deployment, themachine 2700 may operate in the capacity of a server machine, a clientmachine, or both in server-client network environments. In an example,the machine 2700 may act as a peer machine in a peer-to-peer (P2P) (orother distributed) network environment. Further, while only a singlemachine 2700 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein, such as via cloud computing,software as a service (SaaS), or other computer cluster configurations.

Examples, as described herein, may include, or may operate by, logic, anumber of components, or mechanisms. Circuitry is a collection ofcircuits implemented in tangible entities that include hardware (e.g.,simple circuits, gates, logic, etc.). Circuitry membership may beflexible over time and underlying hardware variability. Circuitriesinclude members that may, alone or in combination, perform specifiedoperations when operating. In an example, hardware of the circuitry maybe immutably designed to carry out a specific operation (e.g.,hardwired). In an example, the hardware of the circuitry may includevariably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer-readable mediumphysically modified (e.g., magnetically, electrically, by moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed(for example, from an insulator to a conductor or vice versa). Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuitry in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer-readable medium iscommunicatively coupled to the other components of the circuitry whenthe device is operating. In an example, any of the physical componentsmay be used in more than one member of more than one circuitry. Forexample, under operation, execution units may be used in a first circuitof a first circuitry at one point in time and reused by a second circuitin the first circuitry, or by a third circuit in a second circuitry, ata different time.

The machine (e.g., computer system) 2700 may include a hardwareprocessor 2702 (e.g., a central processing unit (CPU), a hardwareprocessor core, or any combination thereof), a graphics processing unit(GPU) 2703, a main memory 2704, and a static memory 2706, some or all ofwhich may communicate with each other via an interlink (e.g., bus) 2708.The machine 2700 may further include a display device 2710, analphanumeric input device 2712 (e.g., a keyboard), and a user interface(UI) navigation device 2714 (e.g., a mouse). In an example, the displaydevice 2710, alphanumeric input device 2712, and UI navigation device2714 may be a touch screen display. The machine 2700 may additionallyinclude a mass storage device (e.g., drive unit) 2716, a signalgeneration device 2718 (e.g., a speaker), a network interface device2720, and one or more sensors 2721, such as a Global Positioning System(GPS) sensor, compass, accelerometer, or another sensor. The machine2700 may include an output controller 2728, such as a serial (e.g.,universal serial bus (USB)), or other wired or wireless (e.g., infrared(IR), near field communication (NFC), etc.) connection to communicatewith or control one or more peripheral devices (e.g., a printer, cardreader, etc.).

The mass storage device 2716 may include a machine-readable medium 2722on which i s stored one or more sets of data structures or instructions2724 (e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 2724 may alsoreside, completely or at least partially, within the main memory 2704,within the static memory 2706, within the hardware processor 2702, orwithin the GPU 2703 during execution thereof by the machine 2700. In anexample, one or any combination of the hardware processor 2702, the GPU2703, the main memory 2704, the static memory 2706, or the mass storagedevice 2716 may constitute machine-readable media.

While the machine-readable medium 2722 is illustrated as a singlemedium, the term “machine-readable medium” may include a single medium,or multiple media, (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 2724.

The term “machine-readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions 2724 forexecution by the machine 2700 and that cause the machine 2700 to performany one or more of the techniques of the present disclosure, or that iscapable of storing, encoding, or carrying data structures used by orassociated with such instructions 2724. Non-limiting machine-readablemedium examples may include solid-state memories, and optical andmagnetic media. In an example, a massed machine-readable mediumcomprises a machine-readable medium 2722 with a plurality of particleshaving invariant (e.g., rest) mass. Accordingly, massed machine-readablemedia are not transitory propagating signals. Specific examples ofmassed machine-readable media may include non-volatile memory, such assemiconductor memory devices (e.g., Electrically Programmable Read-OnlyMemory (EPROM), Electrically Erasable Programmable Read-Only Memory(EEPROM)) and flash memory devices; magnetic disks, such as internalhard disks and removable disks; magneto-optical disks; and CD-ROM andDVD-ROM disks.

The instructions 2724 may further be transmitted or received over acommunications network 2726 using a transmission medium via the networkinterface device 2720.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

1. A method comprising: accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell; generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, the map of the geographical region having a higher resolution than a resolution of the one or more satellite images; estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire; updating the map of the geographical region based on the probability values for the second cells; and causing presentation of the map in a user interface.
 2. The method of claim 1, comprising: before estimating the probability values, training the machine-learning model with values of the plurality of features from previous fires.
 3. The method of claim 1, wherein the plurality of features comprises: vegetation information for the geographical region; weather data for the geographical region; and land-related features that include land use, slope, aspect, elevation, and soil moisture.
 4. The method of claim 1, wherein the plurality of features comprises hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).
 5. The method of claim 3, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.
 6. The method of claim 1, comprising: estimating, by the machine-learning model, second cells with embers that may ignite fires; and presenting, in the map, an ember area with second cells that comprise embers that may ignite fires.
 7. The method of claim 1, comprising: estimating a fire scar area with second cells that have already burnt; and causing presentation in the user interface of the map with the fire scar area.
 8. The method of claim 1, wherein the one or more satellite images comprise images in a plurality of frequency bands.
 9. The method of claim 1, wherein the first size is a square with 375 meter sides and the second size is a square with 30 meter sides.
 10. The method of claim 1, comprising: receiving observations about a state of the fire in the geographical region; and updating the probability values for the second cells based on the received observations.
 11. A system comprising: a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising: accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell; generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, map of the geographical region having a higher resolution than a resolution of the one or more satellite images; estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire; updating the map of the geographical region based on the probability values for the second cells; and causing presentation of the map in a user interface.
 12. The system of claim 11, wherein the instructions cause the one or more computer processors to perform operations comprising: before estimating the probability values, training the machine-learning model, with values of the plurality of features from previous fires.
 13. The system of claim 11, wherein the plurality of features comprises: vegetation information for the geographical region; weather data for the geographical region; land-related features that include land use, slope, aspect, elevation, and soil moisture; and hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).
 14. The system of claim 13, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.
 15. The system of claim 11, wherein the instructions cause the one or more computer processors to perform operations comprising: estimating, by the machine-learning model, second cells with embers that may ignite fires; and presenting, in the map, an ember area with second cells with embers that may ignite fires.
 16. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell; generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, the map of the geographical region having a higher resolution than a resolution of the one or more satellite images; estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire; updating the map of the geographical region based on the probability values for the second cells; and causing presentation of the map in a user interface.
 17. The non-transitory machine-readable storage medium of claim 16, wherein the machine performs operations comprising: before estimating the probability values, training the machine-learning model, with values of the plurality of features from previous fires.
 18. The non-transitory machine-readable storage medium of claim 16, wherein the plurality of features comprises: vegetation information for the geographical region; weather data for the geographical region; land-related features that include land use, slope, aspect, elevation, and soil moisture; and hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).
 19. The non-transitory machine-readable storage medium of claim 18, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.
 20. The non-transitory machine-readable storage medium of claim 16, wherein the machine performs operations comprising: estimating, by the machine-learning model, second cells with embers that may ignite fires; and presenting, in the map, an ember area with second cells with embers that may ignite fires. 